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Artist In The Machine, Miller, Arthur I.


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Цена: 28160.00T
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При оформлении заказа до: 2025-08-06
Ориентировочная дата поставки: конец Сентября - начало Октября
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Автор: Miller, Arthur I.
Название:  Artist In The Machine
ISBN: 9780262539623
Издательство: MIT Press
Классификация:


ISBN-10: 0262539624
Обложка/Формат: Trade Paperback
Страницы: 432
Вес: 0.59 кг.
Дата издания: 29.09.2020
Язык: English
Размер: 221 x 140 x 25
Читательская аудитория: General (us: trade)
Основная тема: Other Non Fiction
Рейтинг:
Поставляется из: США
Описание: An authority on creativity introduces us to AI-powered computers that are creating art, literature, and music that may well surpass the creations of humans.

Todays computers are composing music that sounds more Bach than Bach, turning photographs into paintings in the style of Van Goghs Starry Night, and even writing screenplays. But are computers truly creative--or are they merely tools to be used by musicians, artists, and writers? In this book, Arthur I. Miller takes us on a tour of creativity in the age of machines.

Miller, an authority on creativity, identifies the key factors essential to the creative process, from the need for introspection to the ability to discover the key problem. He talks to people on the cutting edge of artificial intelligence, encountering computers that mimic the brain and machines that have defeated champions in chess, Jeopardy , and Go. In the central part of the book, Miller explores the riches of computer-created art, introducing us to artists and computer scientists who have, among much else, unleashed an artificial neural network to create a nightmarish, multi-eyed dog-cat; taught AI to imagine; developed a robot that paints; created algorithms for poetry; and produced the worlds first computer-composed musical, Beyond the Fence, staged by Android Lloyd Webber and friends.

But, Miller writes, in order to be truly creative, machines will need to step into the world. He probes the nature of consciousness and speaks to researchers trying to develop emotions and consciousness in computers. Miller argues that computers can already be as creative as humans--and someday will surpass us. But this is not a dystopian account; Miller celebrates the creative possibilities of artificial intelligence in art, music, and literature.



Linear Algebra and Learning from Data

Автор: Strang Gilbert
Название: Linear Algebra and Learning from Data
ISBN: 0692196382 ISBN-13(EAN): 9780692196380
Издательство: Cambridge Academ
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Цена: 66520.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.

Data-driven science and engineering

Автор: Brunton, Steven L. (university Of Washington) Kutz
Название: Data-driven science and engineering
ISBN: 1009098489 ISBN-13(EAN): 9781009098489
Издательство: Cambridge Academ
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Цена: 52790.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Data-driven discovery is revolutionizing how we model, predict, and control complex systems. This text integrates emerging machine learning and data science methods for engineering and science communities. Now with Python and MATLAB (R), new chapters on reinforcement learning and physics-informed machine learning, and supplementary videos and code.

Computer Age Statistical Inference, Student Edition

Автор: Bradley Efron , Trevor Hastie
Название: Computer Age Statistical Inference, Student Edition
ISBN: 1108823416 ISBN-13(EAN): 9781108823418
Издательство: Cambridge Academ
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Цена: 33790.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Computing power has revolutionized the theory and practice of statistical inference. Now in paperback, and fortified with 130 class-tested exercises, this book explains modern statistical thinking from classical theories to state-of-the-art prediction algorithms. Anyone who applies statistical methods to data will value this landmark text.

Mathematics for Machine Learning

Автор: Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong
Название: Mathematics for Machine Learning
ISBN: 110845514X ISBN-13(EAN): 9781108455145
Издательство: Cambridge Academ
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Цена: 42230.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics.

Deep Learning

Автор: Goodfellow Ian, Bengio Yoshua, Courville Aaron
Название: Deep Learning
ISBN: 0262035618 ISBN-13(EAN): 9780262035613
Издательство: MIT Press
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Цена: 90290.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.

"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject."
-- Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.

The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.

Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.


Mining of Massive Datasets

Автор: Leskovec Jure
Название: Mining of Massive Datasets
ISBN: 1108476341 ISBN-13(EAN): 9781108476348
Издательство: Cambridge Academ
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Цена: 71810.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Essential reading for students and practitioners, this book focuses on practical algorithms used to solve key problems in data mining, with exercises suitable for students from the advanced undergraduate level and beyond. This third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs.

Machine Learning Guide for Oil and Gas Using Python: A Step-By-Step Breakdown with Data, Algorithms, Codes, and Applications

Автор: Belyadi Hoss, Haghighat Alireza
Название: Machine Learning Guide for Oil and Gas Using Python: A Step-By-Step Breakdown with Data, Algorithms, Codes, and Applications
ISBN: 0128219297 ISBN-13(EAN): 9780128219294
Издательство: Elsevier Science
Рейтинг:
Цена: 129130.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:

Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications delivers a critical training and resource tool to help engineers understand machine learning theory and practice, specifically referencing use cases in oil and gas. The reference moves from explaining how Python works to step-by-step examples of utilization in various oil and gas scenarios, such as well testing, shale reservoirs and production optimization. Petroleum engineers are quickly applying machine learning techniques to their data challenges, but there is a lack of references beyond the math or heavy theory of machine learning. Machine Learning Guide for Oil and Gas Using Python details the open-source tool Python by explaining how it works at an introductory level then bridging into how to apply the algorithms into different oil and gas scenarios. While similar resources are often too mathematical, this book balances theory with applications, including use cases that help solve different oil and gas data challenges.


Building feature extraction with machine learning :

Автор: Aithal, Bharath H.,
Название: Building feature extraction with machine learning :
ISBN: 1032255331 ISBN-13(EAN): 9781032255330
Издательство: Taylor&Francis
Рейтинг:
Цена: 83690.00 T
Наличие на складе: Поставка под заказ.
Описание: This book focuses on feature extraction methods for optical geospatial data using Machine Learning (ML). It is a practical guide for professionals and graduate students starting a career in information extraction. It explains spatial feature extraction in an easy-to-understand way and includes real case studies.

Smart Applications with Advanced Machine Learning and Human-Centred Problem Design

Автор: Hemanth
Название: Smart Applications with Advanced Machine Learning and Human-Centred Problem Design
ISBN: 3031097521 ISBN-13(EAN): 9783031097522
Издательство: Springer
Рейтинг:
Цена: 186330.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book brings together the most recent, quality research papers accepted and presented in the 3rd International Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME 2021) held in Antalya, Turkey between 1-3 October 2021. Objective of the content is to provide important and innovative research for developments-improvements within different engineering fields, which are highly interested in using artificial intelligence and applied mathematics. As a collection of the outputs from the ICAIAME 2021, the book is specifically considering research outcomes including advanced use of machine learning and careful problem designs on human-centred aspects. In this context, it aims to provide recent applications for real-world improvements making life easier and more sustainable for especially humans. The book targets the researchers, degree students, and practitioners from both academia and the industry.

Graph Representation Learning

Автор: Hamilton, William L.
Название: Graph Representation Learning
ISBN: 3031004604 ISBN-13(EAN): 9783031004605
Издательство: Springer
Рейтинг:
Цена: 51230.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis.This book provides a synthesis and overview of graph representation learning.

Machine Learning

Автор: Kevin Murphy
Название: Machine Learning
ISBN: 0262018020 ISBN-13(EAN): 9780262018029
Издательство: MIT Press
Рейтинг:
Цена: 124150.00 T
Наличие на складе: Невозможна поставка.
Описание:

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.

The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package -- PMTK (probabilistic modeling toolkit) -- that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.


Reinforcement learning

Автор: Sutton, Richard S. Barto, Andrew G.
Название: Reinforcement learning
ISBN: 0262193981 ISBN-13(EAN): 9780262193986
Издательство: MIT Press
Рейтинг:
Цена: 66930.00 T
Наличие на складе: Нет в наличии.
Описание: An account of key ideas and algorithms in reinforcement learning. The discussion ranges from the history of the field`s intellectual foundations to recent developments and applications. Areas studied include reinforcement learning problems in terms of Markov decision problems and solution methods.


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